Neural Proximal Gradient Descent for Compressive Imaging

NeurIPS 2018 Morteza MardaniQingyun SunShreyas VasawanalaVardan PapyanHatef MonajemiJohn PaulyDavid Donoho

Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prior information. Learning a good inverse mapping from training data faces severe challenges, including: (i) scarcity of training data; (ii) need for plausible reconstructions that are physically feasible; (iii) need for fast reconstruction, especially in real-time applications... (read more)

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